Predicting the risk of sarcopenia in Nasopharyngeal Carcinoma patients: Development and assessment of a new predictive nomogram

Predicting the risk of sarcopenia in Nasopharyngeal Carcinoma patients: Development and assessment of a new predictive nomogram | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predicting the risk of sarcopenia in Nasopharyngeal Carcinoma patients: Development and assessment of a new predictive nomogram Ting Liu, Guimei Wang, Chunmei Chen, Lihe He, Rensheng Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4015258/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Sarcopenia, as defined by the Global Leadership Initiative on Malnutrition (GLIM) consensus, serves as a diagnostic indicator for malnutrition and has been shown to influence cancer treatment and clinical results. However, the impact of sarcopenia on individuals diagnosed with nasopharyngeal carcinoma (NPC) remain insufficiently elucidated. The objective of this study was to investigate the prognostic significance of sarcopenia on the survival outcomes of NPC patients and to develop a nomogram. Patients and methods: 545 patients with stage III-IVa NPC were included in this retrospective study and randomly divided into training and validation cohort (381 and 164 patients, respectively). Sarcopenia was defined using the skeletal muscle index (SMI) determined at the C3 level based on baseline MRI. The nomogram was developed utilizing a multivariable Cox model with baseline variables from the training cohort, and validated with the validation cohort. The nomogram's discriminative ability and accuracy were evaluated using the consistency index (C-index), receiver operating characteristic curve (ROC), and calibration plots, while the net benefit was assessed and compared with the TNM clinical stage through decision curve analysis (DCA). Results The results of the multivariate analysis revealed that higher T stage (HR = 2.15, 95% CI: 1.3–3.57, P < 0.01), higher N stage (HR = 2.15, 95% CI: 1.56–2.95, P < 0.01), sarcopenia group (HR = 2.46, 95% CI: 1.58–3.83, P < 0.01), and a history of comorbidities (HR = 1.76, 95% CI: 1.16–2.67, P = 0.01) were identified as independent risk factors that significantly impacted both overall survival (OS). The C-index (0.731 for the training cohort and 0.72 for the validation cohort indicated satisfactory discriminative ability of the nomogram. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts. Moreover, nomograms also showed higher outcomes of DCA and the area under the curve (AUC) compared to TNM clinical stage. Conclusion Sarcopenia, T stage, N stage, and comorbidities were identified as independent prognostic factors for locally advanced NPC (laNPC). The integration of these factors into a nomogram predictive model demonstrated enhanced predictive accuracy. Biological sciences/Cancer/Head and neck cancer Health sciences/Biomarkers/Prognostic markers Sarcopenia1 Nasopharyngeal Carcinoma2 Chemoradiotherapy3 Nomogram4 prognosis5 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Nasopharyngeal carcinoma (NPC) is a malignancy originating from the epithelial cells of the nasopharyngeal mucosa. It exhibits a distinctive geographical prevalence, notably observed in East Asia, Southeast Asia, the Arctic, North Africa, and the Middle East. 1,2 As of 2020, the estimated global incidence of NPC reached 133,354 cases, with 85% occurring in Asia, resulting in 80,008 deaths. 3 Recommended treatment for locally advanced NPC (laNPC) involves a multidisciplinary regimen centered around radiotherapy, encompassing chemoradiotherapy, targeted therapy, and immunotherapy. 4,5,6 In the cases of laNPC patients (those without metastasis), the five-year survival rates post-treatment typically range between 70–90%. 2,7,8 However, 10–25% of patients experience treatment failure, characterized by adverse prognostic factors such as distant metastasis and local recurrence, resulting in dismal prognoses. 9,10 This highlights the limitation of the TNM staging system, considered the foremost prognostic factor in laNPC clinical practice, as patients within the same stage may exhibit diverse prognoses. 11 The TNM staging system stratifies patients solely based on tumor size and the extent of lymph node metastasis while neglecting other critical prognostic factors like nutritional status. Recent studies emphasize the close relationship between a patient's nutritional status and treatment efficacy. 12 Thus, timely and accurate identification of malnourished patients or those at nutritional risk becomes pivotal in devising personalized treatment strategies and follow-up plans. The Skeletal Muscle Mass Index (SMI) serve as key indicators for evaluating patients' nutritional statuses. 13 Within the Global Leadership Initiative on Malnutrition (GLIM) consensus, low skeletal muscle mass, known as sarcopenia, is a diagnostic criterion for malnutrition in hospitalized patients. 14 The SMI is typically calculated by evaluating the cross-sectional area of skeletal muscle tissue at the third lumbar vertebra (L3) in abdominal images and dividing by the square of height (cm2/m2). 14 In this study, the measurement of muscle area at C3 using head and neck scans is identified as the second most prevalent technique for sarcopenia. 15 Utilizing head and neck imaging techniques to quantify muscle area at the C3 level is the second most prevalent approach for evaluating muscle atrophy. 15 Numerous studies have investigated sarcopenia as a prognostic indicator in a range of malignancies. 12 Nevertheless, additional research is needed to elucidate the predictive significance of sarcopenia in NPC patients. The objective of this study is to evaluate the impact of sarcopenia on the survival of laNPC patients. Additionally, we aim to develop a nomogram model based on sarcopenia to predict patient prognosis, aiding in treatment decision-making. 2. Material and methods 2.1 Study design and patient enrollment In this retrospective study, clinical data were collected from a cohort of 545 patients diagnosed with NPC treated at the Department of Radiation Oncology, First Affiliated Hospital of Guangxi Medical University, between January 2016 and December 2019. The staging was conducted using the 8th Edition of the Union for International Cancer Control/American Joint Committee (UICC/AJCC) TNM staging system. Inclusion criteria comprised: 1) histopathologically confirmed NPC diagnosis, 2) staging between III-IVa based on the 8th UICC/AJCC staging system, 3) completion of concurrent chemoradiotherapy, 4) age between 18 and 70 years, 5) available contrast-enhanced MRI scans of the head and neck, 6) absence of prior anti-tumor treatment or other malignancies, and 7) a ECOG Score ranging from 0 to 2. They were randomly allocated into a training cohort (approximately 70%; N = 381) and a validation cohort (remaining 30%; N = 164) for the establishment of a prognostic model. The study strictly adhered to the principles outlined in the Helsinki Declaration, and the research protocol received approval from the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (2024-E178-01, March 13, 2024). 2.2 Treatment regimen A comprehensive assessment was conducted for all patients, including nasopharyngoscopy, magnetic resonance imaging (MRI) for nasopharynx and neck, chest computed tomography (CT), abdominal CT or ultrasound, bone scintigraphy (ECT), electrocardiogram, and blood biochemical tests. Positron emission tomography/computed tomography (PET/CT) was recommended to some patients, tailored to their clinical circumstances. Radiation therapy was administered utilizing intensity-modulated radiation therapy (IMRT) in adherence to international guidelines for CT simulation, target delineation, dose determination, and treatment strategizing. The recommended dosage regimen consisted of the following: PTVnx and PTVrpn (68–74 Gy), PTVnd (66–70 Gy), PTV1 (60–66 Gy), and PTV2 (50–56 Gy), with a treatment frequency of 5 fractions per week, resulting in a totaling of 30–33 fractions. The induction chemotherapy regimens mainly include the GP regimen (gemcitabine + cisplatin), PF regimen (5-fluorouracil + cisplatin), APF regimen (albumin-bound paclitaxel + 5-fluorouracil + cisplatin), and TP regimen (docetaxel + cisplatin), administered every 3 weeks for a total of approximately 2 to 3 cycles. The concurrent chemotherapy protocol involves the administration of single-agent cisplatin every 3 weeks for a total of 2 to 3 cycles. In cases where cisplatin is contraindicated, alternative platinum-based agents are utilized as replacements. 2.3 Measurement and Definition of Sarcopenia SMI is derived by dividing the cross-sectional area of skeletal muscle tissue at the level of the third lumbar vertebra (L3) on abdominal CT images by the square of the height. 14,16 However, the application of this traditional diagnostic method is limited in NPC due to abdominal CT not being a routine diagnostic modality. Conversely, MRI for the nasopharynx and neck stands as the cornerstone of NPC diagnosis and treatment. 15 Consequently, diagnosing sarcopenia in NPC patients using MRI holds greater feasibility. Recent studies have indicated a strong correlation between the muscle cross-section area (CSA) measurements obtained via MRI at the level of the third cervical vertebra (C3) and those obtained from CT scans. This correlation substantiates the feasibility of utilizing MRI for diagnosing sarcopenia. 17,18 Initially, two experienced radiologists performed manual delineation of the muscle contours, including the left Sternocleidomastoid (SCM), right SCM, and Paravertebral Muscles (PVM), as markers, starting from the C3 vertebra on the first image that displayed the entire vertebral arch, transverse processes, and spinous processes in the T2-weighted imaging (T2WI) sequence of the MRI. Subsequently, the total area encompassing these muscles was calculated and termed as CSA. (Fig. 1 ) $$\text{L}3 \text{C}\text{S}\text{A} \left({\text{c}\text{m}}^{2}\right) = 27.304 + 1.363\times \text{C}3 \text{C}\text{S}\text{A} \left({\text{c}\text{m}}^{2}\right)- 0.671\times \text{A}\text{g}\text{e}\left(\text{y}\text{e}\text{a}\text{r}\text{s}\right)+ 0.640\times \text{W}\text{e}\text{i}\text{g}\text{h}\text{t} \left(\text{K}\text{g}\right)+ 26.442\times \text{S}\text{e}\text{x} \left(1 = \text{F}\text{e}\text{m}\text{a}\text{l}\text{e}, 2 = \text{M}\text{a}\text{l}\text{e}\right)$$ The skeletal muscle total CSA at the C3(C3 CSA) cervical vertebral level was converted to CSA at the L3 lumbar vertebral level (L3 CSA) using the formula (Eq. 1). 17,19 The calculation of the SMI was derived from the L3 CSA (Eq. 2). 17,19 $$\text{S}\text{M}\text{I} \left({\text{c}\text{m}}^{2}/{\text{m}}^{2}\right) = \text{L}3 \text{C}\text{S}\text{A} \left({\text{c}\text{m}}^{2}\right) / {\text{H}\text{e}\text{i}\text{g}\text{h}\text{t}}^{2} \left({\text{m}}^{2}\right)$$ Upon examination of existing literature, researchers have identified that 35 studies have implemented gender-specific thresholds, and 13 studies have utilized Receiver Operating Characteristic (ROC) curve analysis to establish precise cutoff values. 15 This study determined the most suitable cutoff values for SMI based on gender, employing ROC curve analysis and Youden’s index, with survival status serving as the primary outcome measure. 17 2.4 Measurement and Definition of Body Mass Index The Body Mass Index (BMI) is calculated by dividing an individual's weight in kilograms by the square of their height in meters (kg/m2). 20 The World Health Organization has established classification standards for BMI, including underweight for the Asian population (BMI 30 kg/m2). 20 Additionally, the European Society for Clinical Nutrition and Metabolism (ESPEN) guidelines indicate that a BMI below 18.5 kg/m2, along with poor general condition, may be indicative of malnutrition. 21 2.5 Follow-up Following the completion of treatment, study participants were scheduled a follow-up strategy with visits every six months over 2 years. The routine visits encompassed thorough physical examinations, blood biochemical tests, nasopharyngoscopy, MRI scans of the head and neck, chest CT, ECT, and abdominal ultrasound. Additional evaluations, such as pathological biopsy or PET/CT scanning were performed if any abnormalities were detected. Subsequently, follow-up appointments were adjusted to every one year in the period of 2 to 5 years after treatment completion. 2.6 Statistical analysis Statistical analyses were conducted using SPSS Statistics version 23.0 (IBM Corp., Chicago) and R software version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria). The optimal cutoff value for SMI was determined via ROC curve analysis and Youden's index, utilizing survival status as the endpoint. Survival curves were generated employing the Kaplan-Meier method and assessed using log-rank tests. Both univariate and multivariate analyses were performed using Cox regression models. Statistical significance was established at P < 0.05 (two-tailed). Based on the outcomes of the multivariate analysis, a nomogram was formulated. The nomogram's discriminative ability and accuracy were evaluated using the consistency index (C-index), receiver operating characteristic curve (ROC), and calibration plots, while the net benefit was assessed and compared with the TNM clinical stage through decision curve analysis (DCA). 3. Results 3.1 Patient Characteristics The baseline clinical characteristics of patients in both the training cohort (n = 381) and the validation cohort (n = 164) are outlined in Table 1 . Statistical analysis indicates that there is no significant difference between the two groups ( P > 0.05). Within the training cohort, there were 285(74.8%) males and 96(25.2) females, with a median age of 46 years (range: 18–69 years). Staging according to the 8th UICC/AJCC staging system revealed 162 cases classified as stage III and 219 cases as stage IVa. The history of smoking is defined as an average of ≥ 1 cigarette/day before admission, and for a duration exceeding 6 months 22 ; Alcohol consumption history is defined as an average alcohol intake of ≥ 100 g/week before admission, and for a duration exceeding 1 year. 23 A total of 135 patients had a positive history of smoking, while 88 patients had a positive history of alcohol consumption. Furthermore, 82 patients presented with comorbidities such as hepatitis B, diabetes, tuberculosis, cardiovascular and cerebrovascular diseases, and immune system disorders. Among the cohort, 183 patients underwent induction chemotherapy followed by concurrent chemoradiotherapy, whereas 24 patients received only concurrent chemoradiotherapy. Additionally, 40 patients, constituting 10.5% of the sample, were identified as malnourished based on their BMI. The criteria for diagnosing sarcopenia were an SMI < 38 cm2/m2 for females and an SMI < 51 cm2/m2 for males. In the training cohort, a total of 169 patients (44.4%) were assigned to the sarcopenia group, while 212 patients (55.6%) were allocated to the non-sarcopenia group. Table 1 Patient demographics and clinical characteristics of the training cohort and the validation cohort Characteristic Training Validation P-value Total(n) (n = 381)(%) (n = 164)(%) Sex Male 396 285 (74.8) 111 (67.7) 0.094 Female 149 96 (25.2) 53 (32.3) Age < 46 years 278 188 (49.3) 90 (54.9) 0.262 ≥ 46 years 267 193 (50.7) 74 (45.1) T stage T2 56 42 (11.0) 14 (8.5) 0.268 T3 248 165 (43.3) 83 (50.6) T4 241 174 (45.7) 67 (40.9) N stage N0 17 9 (2.4) 8 (4.9) 0.229 N1 162 116 (30.4) 46 (28.0) N2 248 168 (44.1) 80 (48.8) N3 118 88 (23.1) 30 (18.3) TNM clinical stage III 239 162 (42.5) 77 (47.0) 0.348 IV 306 219 (57.5) 87 (53.0) sarcopenia Negative 312 212(55.6) 100 (61.0) 0.259 Positive 233 169 (44.4) 64 (39.0) Comorbidities Negative 425 299 (78.5) 126 (76.8) 0.735 Positive 120 82 (21.5) 38 (23.2) BMI(kg/m2) < 18.5 57 40 (10.5) 17(10.4) 1.00 ≥ 18.5 488 341(89.5) 147(89.6) Alcohol Consumption Negative 418 293 (76.9) 125 (76.2) 0.912 Positive 127 88 (23.1) 39 (23.8) Smoking Negative 353 246 (64.6) 107 (65.2) 0.922 Positive 192 135 (35.4) 57 (34.8) Induction Chemotherapy Negative 35 24 (11.6) 11 (12.4) 0.846 Positive 261 183 (88.4) 78 (87.6) 3.2 Sarcopenia and Clinical Characteristics In the training cohort, an analysis revealed that among patients classified as malnourished with a BMI < 18.5 kg/m2, there were 40 cases (10.5%), with 26 (65.0%) identified as sarcopenia patients and 14 (41.9%) as non-sarcopenia patients. Conversely, there were 341 cases (89.5%) of patients with a BMI ≥ 18.5 kg/m2, of which 143 (41.9%) were sarcopenia patients and 198 (58.1%) were non-sarcopenia patients. Additionally, within the subgroup aged ≥ 46 years, there were 119 (61.7%) sarcopenia patients and 74 (38.3%) non-sarcopenia patients. In clinical staging, there were 119 (54.3%) sarcopenia patients in stage IVa, and 100 (46.7%) non-sarcopenia patients. Among patients with complications, there were 47 (57.3%) sarcopenia patients and 35 (42.7%) non-sarcopenia patients. (Table 2 ) Table 2 Sarcopenia and Clinical Characteristics Characteristic sarcopenia Non-sarcopenia P-value Total(n%) (n%) (n%) 381 (100) 119 (44.4) 212 (55.6) Age < 46 years 188 (49.3) 50 (26.6) 138 (73.4) < 0.01 ≥ 46 years 193 (50.7) 119 (61.7) 74 (38.3) TNM clinical stage III 162 (42.5) 50 (30.9) 112 (69.1) 0.016 IV 219 (57.5) 119 (54.3) 100 (46.7) Comorbidities Negative 299 (78.5) 122(40.8) 177 (59.2) < 0.01 Positive 82 (21.5) 47 (57.3) 35 (42.7) BMI(kg/m2) < 18.5 40 (10.5) 26 (65.0) 14(41.9) < 0.01 ≥ 18.5 341 (89.5) 143(41.9) 198(58.1) 3.3 Survival Analysis The training cohort of 381 patients had a median follow-up duration of 53 months (range: 2-100 months). The observed 5-year overall survival (OS) rate was 74.3%. Survival curves indicated a significantly lower OS ( P < 0.01, Fig. 2 A) in the sarcopenia group compared to the non-sarcopenia group. Likewise, patients with older age, later T stage, later N stage, later TNM stage, and comorbidity exhibited notably lower OS rates ( P < 0.01, Fig. 2 B-F). 3.4 Univariate and multivariate analysis of OS In the training cohort, the univariate analysis identified several variables significantly associated with poorer OS (Fig. 3 A). These included older age ( HR = 1.93, 95% CI : 1.29–2.89, P < 0.01), higher T stage ( HR = 1.75, 95% CI : 1.27–2.41, P < 0.01), higher N stage ( HR = 1.95, 95% CI : 1.51–2.54, P < 0.01), higher TNM clinical stage ( HR = 1.91, 95% CI : 1.25–2.91, P < 0.01), sarcopenia group ( HR = 3.33, 95% CI : 2.19–5.05, P < 0.01), and a history of comorbidities ( HR = 2.31, 95% CI : 1.54–3.46, P < 0.01). Regrettably, no significant associations were observed between OS and factors including gender, BMI, history of alcohol consumption, smoking history, and induction chemotherapy ( P ≥ 0.05). The multivariate analysis confirmed several variables independently prognostic for OS(Figure 3 B): higher T stage ( HR = 2.15, 95% CI : 1.3–3.57, P < 0.01), higher N stage ( HR = 2.15, 95% CI : 1.56–2.95, P < 0.01), sarcopenia group ( HR = 2.46, 95% CI : 1.58–3.83, P < 0.01), and a history of comorbidities ( HR = 1.76, 95% CI : 1.16–2.67, P = 0.01). Regrettably, no significant associations were observed between OS and factors including age and TNM clinical stage ( P ≥ 0.05). 3.5 Development and Validation of Nomograms Utilizing these variables, we constructed Nomograms (Fig. 4 ) for predicting OS. Each variable subtype received a score based on a scoring system, and their cumulative scores formed a total score for prognostication. The Nomograms in the training cohort showed a C-index of 0.731 for OS prediction, while the validation cohort showed a C-index of 0.72 for OS prediction. In the training cohort and validation cohort, the calibration plot depicts the strong performance of the Nomogram model (Fig. 5 A-B). Additionally, we compared the predictive accuracy of the Nomograms against the TNM staging system for 5-year OS. DCA results indicated a higher net benefit of the Nomograms over the TNM clinical stage for predicting 5-year OS (Fig. 5 C ). Furthermore, ROC curve analysis demonstrated that the Nomograms predicting 5-year OS (AUC = 0.718, 95% CI: 0.653–0.783) showed higher sensitivity and specificity compared to the TNM clinical stage predicting 5-year OS (AUC = 0.587, 95% CI: 0.526–0.647, Fig. 5 D). 4. Discussion Malignant tumors present a chronic wasting disease, predisposing patients to a heightened risk of malnutrition owing to abnormal tumor cell metabolism and adverse reactions from anti-tumor therapies. 24 The severe impact of malnutrition significantly hampers the effectiveness of anti-tumor treatments, exerting a pervasive influence throughout the disease trajectory and remaining a primary adverse factor in the clinical outcomes of cancer patients. 24 The location of the tumor is a primary determinant of malnutrition, with head and neck cancer being particularly common in this regard. 25 Therefore, timely and precise diagnosis of malnutrition holds crucial importance in enhancing nutritional status and improving prognostic outcomes. The skeletal muscle mass index (SMI) serves as a key indicator in evaluating the nutritional status of patients, with low skeletal muscle mass commonly identified as sarcopenia. 13,14 The present study found that patients with sarcopenia exhibited significantly lower OS rates compared to those without sarcopenia, suggesting sarcopenia as a negative prognostic factor for individuals with laNPC. The underlying reasons for this association may be multifactorial. 26,27 Given the crucial role muscles play in metabolic regulation and immunity, muscle atrophy increases the risk of tumor progression. 12,15 Skeletal muscles can function as an endocrine organ, releasing myokines, cytokines, and proteins via autocrine, paracrine, and endocrine pathways, influencing signaling between muscles and other systems. 28,29 Additionally, the microenvironment of skeletal muscles comprises neutrophils, monocytes, and T cells, participating in metabolic processes, wherein sarcopenia in patients might imply compromised host immune defense, leading to reduced sensitivity to immune therapy. 30 Furthermore, sarcopenia is primarily induced by tumor metabolism, where the additional protein consumed by tumors reduces the available protein for other tissues, leading to sarcopenia and cachexia, ultimately resulting in adverse prognoses. 31 Previous research has demonstrated consistent findings. A study conducted by Liu et al. revealed that sarcopenia serves as a distinct risk factor for OS and PFS ( P < 0.05). Specifically, individuals with sarcopenia exhibited inferior OS ( HR = 2.00, 95% CI : 1.54 ~ 2.60, P < 0.001) and PFS ( HR = 1.67, 95% CI : 1.35 ~ 2.07, P < 0.001) outcomes compared to their non-sarcopenic counterparts. 17 In the present study, subgroup analysis sarcopenia and clinical characteristics, it was observed that advanced age, TNM clinical stage, and comorbidities were correlated with a heightened prevalence of sarcopenia. This suggests that individuals with adverse prognostic factors are at an elevated risk of malnutrition. This assertion aligns with the perspectives of prior researchers, indicating that the later stages of disease progression are associated with an increased susceptibility to malnutrition due to the interplay of factors such as tumor burden, inflammatory response, decreased caloric consumption, and impaired nutrient absorption. 25 In the current study, an examination was conducted on the influence of BMI on the prognosis of NPC. No significant association was found, which is an intriguing phenomenon. BMI is widely used to assess the nutritional status of adults. However, the relationship between pretreatment BMI and prognosis in NPC patients remains unclear. This may be because BMI does not accurately reflect the composition of body constituents and their changes: it cannot distinguish between fat and muscle mass, nor can it differentiate between fluid volume accumulation in the interstitium and actual body tissue. 32 Pan et al. conducted a study to evaluate the nutritional status of patients diagnosed with laNPC. 33 The findings revealed that a minority of patients (3.8%) had a BMI below 18.5 kg/m2, while a significant proportion (46.9%) were identified as being at nutritional risk based on the NRS 2002 screening tool, and over half (53.1%) were classified as malnourished according to the PG-SGA assessment. Furthermore, a notable percentage (30.8%) of patients exhibited low muscle mass, with statistically significant variances observed (P < 0.001). 33 Similar findings were noted in the current study, wherein the prevalence of sarcopenia in the training cohort was 44.4%, which was notably higher than the incidence rate of BMI < 18.5 kg/m2 (10.5%). Furthermore, among patients with a BMI ≥ 18.5 kg/m2, 41.9% still exhibited sarcopenia. These results suggest that relying solely on BMI for evaluating malnutrition in patients may have certain constraints as a prognostic indicator. The study revealed significant associations between patient prognosis and various factors, including age, T stage, N stage, TNM clinical stage, and comorbidities such as hepatitis B, diabetes, cardiovascular diseases, tuberculosis, and immune system disorders. Notably, hepatitis B exhibited the highest incidence rate among the comorbidities studied, likely due to the high prevalence of chronic HBV infection in Southern China, where the HBsAg seropositivity rate in the general population ranges from 10–12%. 34 Therefore, hepatitis B is also an important comorbidity in patients with nasopharyngeal carcinoma in Southern China. The poor prognosis observed in patients with hepatitis B may be attributed to immune dysfunction, including hepatitis B-related nephritis, elevated expression of the immune inhibitory protein programmed cell death protein 1 (PD-1) on total T cells, and reduced proliferative capacity of activated B cells, suggesting impaired immune function in individuals with hepatitis B. 35,36 Therefore, the interplay between hepatitis B and the immune response of NPC cells could have nuanced implications, potentially compromising the immune surveillance of malignant cell hosts in patients with NPC. The 8th Edition of the UICC/AJCC TNM staging system utilizes the parameters of "Tumor" (T), "Node" (N), and "Metastasis" (M) to delineate the anatomical scope of tumors. In particular, "T" characterizes the size and invasiveness of the primary tumor, "N" signifies the existence and extent of regional lymph node metastasis (LN(s)), and "M" denotes the presence or absence of distant metastasis. 37 In the current investigation, the T stage, N stage, and TNM clinical stage were identified as factors associated with patient prognosis in univariate analysis. However, in multivariate analysis, the TNM clinical stage did not demonstrate statistical significance. This lack of significance may be attributed to the interdependent nature of the T stage, N stage, and M stage in determining the TNM clinical stage, resulting in a combined influence that neutralizes their individual impact on prognosis in multivariate analysis. These findings indicate that the study population in the current research is reflective of the broader population of laNPC patients, thereby bolstering the validity of the analytical results. The 8th Edition of the UICC/AJCC TNM staging system is limited in its consideration of the biological heterogeneity of NPA patients, focusing primarily on anatomical structure. 11,38 The variability in patient outcomes within the same stage hinders the prognostic accuracy of the TNM staging system. The present study highlights the significant relationship between nutritional status and tumor prognosis. Consequently, a novel nomogram model was developed to forecast the overall survival of laNPC patients, and its predictive performance was compared to that of the TNM clinical stage. The findings suggest that the nomogram model demonstrates superior accuracy in prognostic prediction compared to the TNM clinical stage. This straightforward scoring system enables clinicians and patients to discern distinct subgroups of patients with varying survival risks, facilitating the selection of tailored treatment and care strategies. Enhanced follow-up measures can be implemented for high-risk patients. Our study acknowledges several limitations. Primarily, the nomogram construction relied on data solely from a single center in a specific geographic region, potentially introducing bias. Thus, further validation using external datasets is imperative to establish its generalizability. Secondly, determining optimal cut-off values for the sarcopenia warrant extensive investigation through large-scale clinical studies to enhance predictive precision. Moreover, our study primarily focuses on the relationship between sarcopenia and prognosis at the onset of patient treatment, lacking exploration into their dynamic interaction throughout the treatment trajectory. 5. Conclusion In the present study, sarcopenia was identified as a prognostic factor for patients with laNPC. Patients with sarcopenia demonstrated lower overall survival rates compared to non-sarcopenic patients. Furthermore, T stage, N stage, and comorbidities were also found to be prognostically correlated factors in this investigation. The integration of these prognostic factors into a constructed nomogram predictive model exhibited superior predictive capacity. Declarations Funding: This work was supported by grants from the National Natural Science Foundation of China (No. 71964003, 81460460, 81760542, 82260467), The Natural Science Foundation of Guangxi Zhuang Autonomous Region (No. 2018JJA141048), The central government guide local science and technology development projects (ZY18057006). Competing Interests : The authors have no relevant financial or non-financial interests to disclose. Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (2024-E178-01, March 13, 2024). Consent to publish: Because of the retrospective design of the study, the requirement for informed consent for publication of human research participants was waived, Approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (2024-E178-01, March 13, 2024). Data Availability Statement: Data for this study are available from the corresponding author upon reasonable request. Disclosure The author(s) report no conflicts of interest in this work. Author Contribution TL: Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft. GMW: Data curation, Formal analysis, Investigation. CMC: Investigation, Software, Writing – original draft. LHH: Conceptualization, Methodology, Validation, Visualization. RSW: Investigation, Supervision, Validation, Writing – review & editing. All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved the submitted version. References Chang E T, Ye W, Zeng Y-X, and Adami H-O. The Evolving Epidemiology of Nasopharyngeal Carcinoma. Cancer Epidemiology, Biomarkers & Prevention 30 (2021) 1035–1047. doi: 10.1158/1055-9965.Epi-20-1702 . Chen Y-P, Chan A T C, Le Q-T, Blanchard P, Sun Y, and Ma J. Nasopharyngeal carcinoma. The Lancet 394 (2019) 64–80. doi: 10.1016/s0140-6736(19)30956-0 . Sung H, Ferlay J, Siegel R L, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians 71 (2021) 209–249. doi: 10.3322/caac.21660 . Pan J J, Ng W T, Zong J F, Lee S W M, Choi H C W, Chan L L K, et al. 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JAMA Netw Open 4 (2021) e2124721. doi: 10.1001/jamanetworkopen.2021.24721 . de Bree R, van Beers M A, and Schaeffers A W M A. Sarcopenia and its impact in head and neck cancer treatment. Current Opinion in Otolaryngology & Head & Neck Surgery 30 (2022) 87–93. doi: 10.1097/moo.0000000000000792 . Sánchez-Rodríguez D, De Meester D, Minon L, Claessens M, Gümüs N, Lieten S, et al. Association between Malnutrition Assessed by the Global Leadership Initiative on Malnutrition Criteria and Mortality in Older People: A Scoping Review. International Journal of Environmental Research and Public Health 20 (2023). doi: 10.3390/ijerph20075320 . Cederholm T, Jensen G L, Correia M I T D, Gonzalez M C, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition – A consensus report from the global clinical nutrition community. Clinical Nutrition 38 (2019) 1–9. doi: 10.1016/j.clnu.2018.08.002 . Pereira L C, Jovanovic N, Chinnery T, Mattonen S A, Palma D A, Doyle P C, et al. Sarcopenia in head and neck cancer: A scoping review. Plos One 17 (2022). doi: 10.1371/journal.pone.0278135 . Cruz-Jentoft A J, and Sayer A A. Sarcopenia. The Lancet 393 (2019) 2636–2646. doi: 10.1016/s0140-6736(19)31138-9 . Liu S, Zou Y, Zhong M, Li T, Cao Y, Wang R, et al. Prognostic significance of MRI-defined sarcopenia in patients with nasopharyngeal carcinoma: A propensity score matched analysis of real-world data. Radiotherapy and Oncology 188 (2023). doi: 10.1016/j.radonc.2023.109904 . Zwart A T, Becker J-N, Lamers M J, Dierckx R A J O, de Bock G H, Halmos G B, et al. Skeletal muscle mass and sarcopenia can be determined with 1.5-T and 3-T neck MRI scans, in the event that no neck CT scan is performed. European Radiology 31 (2020) 4053–4062. doi: 10.1007/s00330-020-07440-1 . Swartz J E, Pothen A J, Wegner I, Smid E J, Swart K M A, de Bree R, et al. Feasibility of using head and neck CT imaging to assess skeletal muscle mass in head and neck cancer patients. Oral Oncology 62 (2016) 28–33. doi: 10.1016/j.oraloncology.2016.09.006 . Consultation W H O E. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363 (2004) 157–163. doi: 10.1016/S0140-6736(03)15268-3 . Cederholm T, Barazzoni R, Austin P, Ballmer P, Biolo G, Bischoff S C, et al. ESPEN guidelines on definitions and terminology of clinical nutrition. Clin Nutr 36 (2017) 49–64. doi: 10.1016/j.clnu.2016.09.004 . Lindbohm J V, Kaprio J, Jousilahti P, Salomaa V, and Korja M. Sex, Smoking, and Risk for Subarachnoid Hemorrhage. Stroke 47 (2016) 1975–1981. doi: 10.1161/STROKEAHA.116.012957 . Wood A M, Kaptoge S, Butterworth A S, Willeit P, Warnakula S, Bolton T, et al. Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies. Lancet 391 (2018) 1513–1523. doi: 10.1016/S0140-6736(18)30134-X . Muscaritoli M, Arends J, Bachmann P, Baracos V, Barthelemy N, Bertz H, et al. ESPEN practical guideline: Clinical Nutrition in cancer. Clinical Nutrition 40 (2021) 2898–2913. doi: 10.1016/j.clnu.2021.02.005 . Bossi P, Delrio P, Mascheroni A, and Zanetti M. The Spectrum of Malnutrition/Cachexia/Sarcopenia in Oncology According to Different Cancer Types and Settings: A Narrative Review. Nutrients 13 (2021). doi: 10.3390/nu13061980 . Schmidt S F, Rohm M, Herzig S, and Berriel Diaz M. Cancer Cachexia: More Than Skeletal Muscle Wasting. Trends in Cancer 4 (2018) 849–860. doi: 10.1016/j.trecan.2018.10.001 . Argilés J M, Busquets S, Felipe A, and López-Soriano F J. Molecular mechanisms involved in muscle wasting in cancer and ageing: cachexia versus sarcopenia. The International Journal of Biochemistry & Cell Biology 37 (2005) 1084–1104. doi: 10.1016/j.biocel.2004.10.003 . Manole E, Ceafalan L C, Popescu B O, Dumitru C, and Bastian A E. Myokines as Possible Therapeutic Targets in Cancer Cachexia. Journal of Immunology Research 2018 (2018) 1–9. doi: 10.1155/2018/8260742 . Halmos T, and Suba I. The secretory mechanism of the muscular system and its role in the metabolism and utilization of energy. Orvosi Hetilap 155 (2014) 1469–1477. doi: 10.1556/oh.2014.29959 . VanderVeen B N, Murphy E A, and Carson J A. The Impact of Immune Cells on the Skeletal Muscle Microenvironment During Cancer Cachexia. Frontiers in Physiology 11 (2020). doi: 10.3389/fphys.2020.01037 . Zhang Q, Song M M, Zhang X, Ding J S, Ruan G T, Zhang X W, et al. Association of systemic inflammation with survival in patients with cancer cachexia: results from a multicentre cohort study. Journal of Cachexia, Sarcopenia and Muscle 12 (2021) 1466–1476. doi: 10.1002/jcsm.12761 . Jin X, Hu R, Guo H, Ding C, Pi G, and Tian M. Pretreatment Body Mass Index (BMI) as an Independent Prognostic Factor in Nasopharyngeal Carcinoma Survival: A Systematic Review and Meta-Analysis. Nutrition and Cancer 74 (2022) 3457–3467. doi: 10.1080/01635581.2022.2084557 . Pan X, Liu H, Feng G, Xiao J, Wang M, Liu H, et al. Role of Muscle Mass and Nutritional Assessment Tools in Evaluating the Nutritional Status of Patients With Locally Advanced Nasopharyngeal Carcinoma. Front Nutr 8 (2021) 567085. doi: 10.3389/fnut.2021.567085 . Te H S, and Jensen D M. Epidemiology of hepatitis B and C viruses: a global overview. Clin Liver Dis 14 (2010) 1–21, vii. doi: 10.1016/j.cld.2009.11.009 . Weng J J, Wei J Z, Li M, Lu J L, Qin Y D, Jiang H, et al. Effects of hepatitis B virus infection and antiviral therapy on the clinical prognosis of nasopharyngeal carcinoma. Cancer Med 9 (2020) 541–551. doi: 10.1002/cam4.2715 . Li H, Cao D, Li S, Chen B, Zhang Y, Zhu Y, et al. Synergistic Association of Hepatitis B Surface Antigen and Plasma Epstein-Barr Virus DNA Load on Distant Metastasis in Patients With Nasopharyngeal Carcinoma. JAMA Netw Open 6 (2023) e2253832. doi: 10.1001/jamanetworkopen.2022.53832 . Ng W T, Yuen K T, Au K H, Chan O S, and Lee A W. Staging of nasopharyngeal carcinoma–the past, the present and the future. Oral Oncol 50 (2014) 549–554. doi: 10.1016/j.oraloncology.2013.06.003 . Liu K, and Wang J. Developing a nomogram model and prognostic analysis of nasopharyngeal squamous cell carcinoma patients: a population-based study. Journal of Cancer Research and Clinical Oncology 149 (2023) 12165–12175. doi: 10.1007/s00432-023-05120-3 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4015258","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":280535711,"identity":"39b7bd48-a9de-41aa-9155-4a25ce0a9efb","order_by":0,"name":"Ting Liu","email":"","orcid":"","institution":"First Affiliated Hospital of GuangXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Liu","suffix":""},{"id":280535712,"identity":"08f93c92-e191-4224-8af4-06ca8b7d4965","order_by":1,"name":"Guimei Wang","email":"","orcid":"","institution":"First Affiliated Hospital of GuangXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guimei","middleName":"","lastName":"Wang","suffix":""},{"id":280535713,"identity":"06e5d155-5cc1-4938-a950-ebcbb125ec05","order_by":2,"name":"Chunmei Chen","email":"","orcid":"","institution":"First Affiliated Hospital of GuangXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chunmei","middleName":"","lastName":"Chen","suffix":""},{"id":280535714,"identity":"b8b04043-fb49-4d44-aaa5-97650ceeb197","order_by":3,"name":"Lihe He","email":"","orcid":"","institution":"First Affiliated Hospital of GuangXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lihe","middleName":"","lastName":"He","suffix":""},{"id":280535716,"identity":"52bf9633-aa23-4447-8268-8016df716158","order_by":4,"name":"Rensheng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYFACxuYHHwz+y7GxNzYc+FAhISdPWAtzm+GMCmZjfp7DBx/OOGNhbNhAUAt7gzTPGebEmTPcko152yoSGQ4Q0GDef7DBcGYbm7HBDR4zyZnzJBIYG5gfPrqBR4vMjcSGBx/beOQMbveYSXzcJpHHzsBmbJyDR4uEBCPIFgljgztngLZskyhmbOBhk8arhf9ggzRvm0Hihhs5ZtK8cyQSGw4Q0sKQCPJ+AtD7aUDvNxCjRSIRFMgHoIF8TMLYsJmQX/iPPwZG5QFoVNbUycmzNz98jE8LFsBMmvJRMApGwSgYBVgAACBKUrQJ8aCyAAAAAElFTkSuQmCC","orcid":"","institution":"First Affiliated Hospital of GuangXi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Rensheng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-03-05 05:04:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4015258/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4015258/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53033295,"identity":"41b484bd-5d11-441c-88b7-d41244416ef0","added_by":"auto","created_at":"2024-03-19 20:21:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":167128,"visible":true,"origin":"","legend":"\u003cp\u003eOn the T2-weighted imaging sequence of the MRI, the first image displaying the entire vertebral arch, transverse processes, and spinous processes was selected. Manual delineation of muscle contours was performed, encompassing the left sternocleidomastoid muscle (1), right sternocleidomastoid muscle (2), and paravertebral muscles (3). Subsequently, the total area of these muscles was calculated.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4015258/v1/88bd8b0fc67246b86f2ded4d.png"},{"id":53033691,"identity":"30ad5b23-97da-4f26-80b9-ea919811cdfa","added_by":"auto","created_at":"2024-03-19 20:29:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":176090,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan−Meier curves of 5-year overall survival (OS) according to sarcopenia (A); age (B), T stage (C), N stage (D), TNM clinical stage (E), and comorbidity (F).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4015258/v1/d8fe9f36c844399155711f50.jpg"},{"id":53033293,"identity":"a00d1275-cd66-477c-9b4b-65d75033da9f","added_by":"auto","created_at":"2024-03-19 20:21:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70970,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Univariate analysis on prognostic factors for OS of the NPC patients in the training cohort; (B) Multivariate analysis on prognostic factors for OS of the NPC patients in the training cohort.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4015258/v1/4e7c8ff7bbb59f3828ab3a41.jpg"},{"id":53033292,"identity":"aa22b589-4d5f-4228-860c-d3c532bd6138","added_by":"auto","created_at":"2024-03-19 20:21:35","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":48988,"visible":true,"origin":"","legend":"\u003cp\u003eNomograms predicting 5-year overall survival in the training cohort.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4015258/v1/04932636f5c43da88f6215f4.jpg"},{"id":53033296,"identity":"f07ef7de-5498-4545-a38d-847d9fb1a8f1","added_by":"auto","created_at":"2024-03-19 20:21:35","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":85258,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Calibration plot for 5-year overall survival (OS) prediction in the training cohort, with a c-index of 0.731; (B) Calibration plot for 5-year OS prediction in the validation cohort, with a c-index of 0.72; (C) The decision curves of 5-year overall survival (A) by the nomogram and the clinical TNM staging system in the training cohort; (D)The time-dependent receiver operating characteristic curves of 5-year overall survival by the nomogram and the clinical TNM staging system in the training cohort.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4015258/v1/1c796c73f60349bf336df6b6.jpg"},{"id":53830567,"identity":"dc4b5d0d-0747-4478-b79f-b37e040cb6b6","added_by":"auto","created_at":"2024-04-01 04:35:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1024553,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4015258/v1/c3807a82-4672-4420-b007-c39a6528018e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting the risk of sarcopenia in Nasopharyngeal Carcinoma patients: Development and assessment of a new predictive nomogram","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNasopharyngeal carcinoma (NPC) is a malignancy originating from the epithelial cells of the nasopharyngeal mucosa. It exhibits a distinctive geographical prevalence, notably observed in East Asia, Southeast Asia, the Arctic, North Africa, and the Middle East.\u003csup\u003e1,2\u003c/sup\u003e As of 2020, the estimated global incidence of NPC reached 133,354 cases, with 85% occurring in Asia, resulting in 80,008 deaths.\u003csup\u003e3\u003c/sup\u003e Recommended treatment for locally advanced NPC (laNPC) involves a multidisciplinary regimen centered around radiotherapy, encompassing chemoradiotherapy, targeted therapy, and immunotherapy.\u003csup\u003e4,5,6\u003c/sup\u003e In the cases of laNPC patients (those without metastasis), the five-year survival rates post-treatment typically range between 70\u0026ndash;90%.\u003csup\u003e2,7,8\u003c/sup\u003e However, 10\u0026ndash;25% of patients experience treatment failure, characterized by adverse prognostic factors such as distant metastasis and local recurrence, resulting in dismal prognoses.\u003csup\u003e9,10\u003c/sup\u003e This highlights the limitation of the TNM staging system, considered the foremost prognostic factor in laNPC clinical practice, as patients within the same stage may exhibit diverse prognoses.\u003csup\u003e11\u003c/sup\u003e The TNM staging system stratifies patients solely based on tumor size and the extent of lymph node metastasis while neglecting other critical prognostic factors like nutritional status. Recent studies emphasize the close relationship between a patient's nutritional status and treatment efficacy.\u003csup\u003e12\u003c/sup\u003e Thus, timely and accurate identification of malnourished patients or those at nutritional risk becomes pivotal in devising personalized treatment strategies and follow-up plans.\u003c/p\u003e \u003cp\u003eThe Skeletal Muscle Mass Index (SMI) serve as key indicators for evaluating patients' nutritional statuses.\u003csup\u003e13\u003c/sup\u003e Within the Global Leadership Initiative on Malnutrition (GLIM) consensus, low skeletal muscle mass, known as sarcopenia, is a diagnostic criterion for malnutrition in hospitalized patients.\u003csup\u003e14\u003c/sup\u003e The SMI is typically calculated by evaluating the cross-sectional area of skeletal muscle tissue at the third lumbar vertebra (L3) in abdominal images and dividing by the square of height (cm2/m2).\u003csup\u003e14\u003c/sup\u003e In this study, the measurement of muscle area at C3 using head and neck scans is identified as the second most prevalent technique for sarcopenia.\u003csup\u003e15\u003c/sup\u003e Utilizing head and neck imaging techniques to quantify muscle area at the C3 level is the second most prevalent approach for evaluating muscle atrophy.\u003csup\u003e15\u003c/sup\u003e Numerous studies have investigated sarcopenia as a prognostic indicator in a range of malignancies.\u003csup\u003e12\u003c/sup\u003e Nevertheless, additional research is needed to elucidate the predictive significance of sarcopenia in NPC patients.\u003c/p\u003e \u003cp\u003eThe objective of this study is to evaluate the impact of sarcopenia on the survival of laNPC patients. Additionally, we aim to develop a nomogram model based on sarcopenia to predict patient prognosis, aiding in treatment decision-making.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and patient enrollment\u003c/h2\u003e \u003cp\u003eIn this retrospective study, clinical data were collected from a cohort of 545 patients diagnosed with NPC treated at the Department of Radiation Oncology, First Affiliated Hospital of Guangxi Medical University, between January 2016 and December 2019. The staging was conducted using the 8th Edition of the Union for International Cancer Control/American Joint Committee (UICC/AJCC) TNM staging system. Inclusion criteria comprised: 1) histopathologically confirmed NPC diagnosis, 2) staging between III-IVa based on the 8th UICC/AJCC staging system, 3) completion of concurrent chemoradiotherapy, 4) age between 18 and 70 years, 5) available contrast-enhanced MRI scans of the head and neck, 6) absence of prior anti-tumor treatment or other malignancies, and 7) a ECOG Score ranging from 0 to 2. They were randomly allocated into a training cohort (approximately 70%; N\u0026thinsp;=\u0026thinsp;381) and a validation cohort (remaining 30%; N\u0026thinsp;=\u0026thinsp;164) for the establishment of a prognostic model.\u003c/p\u003e \u003cp\u003e The study strictly adhered to the principles outlined in the Helsinki Declaration, and the research protocol received approval from the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (2024-E178-01, March 13, 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Treatment regimen\u003c/h2\u003e \u003cp\u003eA comprehensive assessment was conducted for all patients, including nasopharyngoscopy, magnetic resonance imaging (MRI) for nasopharynx and neck, chest computed tomography (CT), abdominal CT or ultrasound, bone scintigraphy (ECT), electrocardiogram, and blood biochemical tests. Positron emission tomography/computed tomography (PET/CT) was recommended to some patients, tailored to their clinical circumstances.\u003c/p\u003e \u003cp\u003e Radiation therapy was administered utilizing intensity-modulated radiation therapy (IMRT) in adherence to international guidelines for CT simulation, target delineation, dose determination, and treatment strategizing. The recommended dosage regimen consisted of the following: PTVnx and PTVrpn (68\u0026ndash;74 Gy), PTVnd (66\u0026ndash;70 Gy), PTV1 (60\u0026ndash;66 Gy), and PTV2 (50\u0026ndash;56 Gy), with a treatment frequency of 5 fractions per week, resulting in a totaling of 30\u0026ndash;33 fractions.\u003c/p\u003e \u003cp\u003eThe induction chemotherapy regimens mainly include the GP regimen (gemcitabine\u0026thinsp;+\u0026thinsp;cisplatin), PF regimen (5-fluorouracil\u0026thinsp;+\u0026thinsp;cisplatin), APF regimen (albumin-bound paclitaxel\u0026thinsp;+\u0026thinsp;5-fluorouracil\u0026thinsp;+\u0026thinsp;cisplatin), and TP regimen (docetaxel\u0026thinsp;+\u0026thinsp;cisplatin), administered every 3 weeks for a total of approximately 2 to 3 cycles. The concurrent chemotherapy protocol involves the administration of single-agent cisplatin every 3 weeks for a total of 2 to 3 cycles. In cases where cisplatin is contraindicated, alternative platinum-based agents are utilized as replacements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Measurement and Definition of Sarcopenia\u003c/h2\u003e \u003cp\u003eSMI is derived by dividing the cross-sectional area of skeletal muscle tissue at the level of the third lumbar vertebra (L3) on abdominal CT images by the square of the height.\u003csup\u003e14,16\u003c/sup\u003e However, the application of this traditional diagnostic method is limited in NPC due to abdominal CT not being a routine diagnostic modality. Conversely, MRI for the nasopharynx and neck stands as the cornerstone of NPC diagnosis and treatment.\u003csup\u003e15\u003c/sup\u003e Consequently, diagnosing sarcopenia in NPC patients using MRI holds greater feasibility. Recent studies have indicated a strong correlation between the muscle cross-section area (CSA) measurements obtained via MRI at the level of the third cervical vertebra (C3) and those obtained from CT scans. This correlation substantiates the feasibility of utilizing MRI for diagnosing sarcopenia.\u003csup\u003e17,18\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eInitially, two experienced radiologists performed manual delineation of the muscle contours, including the left Sternocleidomastoid (SCM), right SCM, and Paravertebral Muscles (PVM), as markers, starting from the C3 vertebra on the first image that displayed the entire vertebral arch, transverse processes, and spinous processes in the T2-weighted imaging (T2WI) sequence of the MRI. Subsequently, the total area encompassing these muscles was calculated and termed as CSA. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{L}3 \\text{C}\\text{S}\\text{A} \\left({\\text{c}\\text{m}}^{2}\\right) = 27.304 + 1.363\\times \\text{C}3 \\text{C}\\text{S}\\text{A} \\left({\\text{c}\\text{m}}^{2}\\right)- 0.671\\times \\text{A}\\text{g}\\text{e}\\left(\\text{y}\\text{e}\\text{a}\\text{r}\\text{s}\\right)+ 0.640\\times \\text{W}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t} \\left(\\text{K}\\text{g}\\right)+ 26.442\\times \\text{S}\\text{e}\\text{x} \\left(1 = \\text{F}\\text{e}\\text{m}\\text{a}\\text{l}\\text{e}, 2 = \\text{M}\\text{a}\\text{l}\\text{e}\\right)$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe skeletal muscle total CSA at the C3(C3 CSA) cervical vertebral level was converted to CSA at the L3 lumbar vertebral level (L3 CSA) using the formula (Eq.\u0026nbsp;1).\u003csup\u003e17,19\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe calculation of the SMI was derived from the L3 CSA (Eq.\u0026nbsp;2).\u003csup\u003e17,19\u003c/sup\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\text{S}\\text{M}\\text{I} \\left({\\text{c}\\text{m}}^{2}/{\\text{m}}^{2}\\right) = \\text{L}3 \\text{C}\\text{S}\\text{A} \\left({\\text{c}\\text{m}}^{2}\\right) / {\\text{H}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t}}^{2} \\left({\\text{m}}^{2}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eUpon examination of existing literature, researchers have identified that 35 studies have implemented gender-specific thresholds, and 13 studies have utilized Receiver Operating Characteristic (ROC) curve analysis to establish precise cutoff values.\u003csup\u003e15\u003c/sup\u003e This study determined the most suitable cutoff values for SMI based on gender, employing ROC curve analysis and Youden\u0026rsquo;s index, with survival status serving as the primary outcome measure.\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Measurement and Definition of Body Mass Index\u003c/h2\u003e \u003cp\u003eThe Body Mass Index (BMI) is calculated by dividing an individual's weight in kilograms by the square of their height in meters (kg/m2).\u003csup\u003e20\u003c/sup\u003e The World Health Organization has established classification standards for BMI, including underweight for the Asian population (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m2), normal weight (18.5\u0026ndash;24.9 kg/m2), overweight (25-29.9 kg/m2), and obesity (\u0026gt;\u0026thinsp;30 kg/m2).\u003csup\u003e20\u003c/sup\u003e Additionally, the European Society for Clinical Nutrition and Metabolism (ESPEN) guidelines indicate that a BMI below 18.5 kg/m2, along with poor general condition, may be indicative of malnutrition.\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Follow-up\u003c/h2\u003e \u003cp\u003eFollowing the completion of treatment, study participants were scheduled a follow-up strategy with visits every six months over 2 years. The routine visits encompassed thorough physical examinations, blood biochemical tests, nasopharyngoscopy, MRI scans of the head and neck, chest CT, ECT, and abdominal ultrasound. Additional evaluations, such as pathological biopsy or PET/CT scanning were performed if any abnormalities were detected. Subsequently, follow-up appointments were adjusted to every one year in the period of 2 to 5 years after treatment completion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using SPSS Statistics version 23.0 (IBM Corp., Chicago) and R software version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria). The optimal cutoff value for SMI was determined via ROC curve analysis and Youden's index, utilizing survival status as the endpoint. Survival curves were generated employing the Kaplan-Meier method and assessed using log-rank tests. Both univariate and multivariate analyses were performed using Cox regression models. Statistical significance was established at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed). Based on the outcomes of the multivariate analysis, a nomogram was formulated. The nomogram's discriminative ability and accuracy were evaluated using the consistency index (C-index), receiver operating characteristic curve (ROC), and calibration plots, while the net benefit was assessed and compared with the TNM clinical stage through decision curve analysis (DCA).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient Characteristics\u003c/h2\u003e \u003cp\u003eThe baseline clinical characteristics of patients in both the training cohort (n\u0026thinsp;=\u0026thinsp;381) and the validation cohort (n\u0026thinsp;=\u0026thinsp;164) are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Statistical analysis indicates that there is no significant difference between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Within the training cohort, there were 285(74.8%) males and 96(25.2) females, with a median age of 46 years (range: 18\u0026ndash;69 years). Staging according to the 8th UICC/AJCC staging system revealed 162 cases classified as stage III and 219 cases as stage IVa. The history of smoking is defined as an average of \u0026ge;\u0026thinsp;1 cigarette/day before admission, and for a duration exceeding 6 months\u003csup\u003e22\u003c/sup\u003e; Alcohol consumption history is defined as an average alcohol intake of \u0026ge;\u0026thinsp;100 g/week before admission, and for a duration exceeding 1 year.\u003csup\u003e23\u003c/sup\u003e A total of 135 patients had a positive history of smoking, while 88 patients had a positive history of alcohol consumption. Furthermore, 82 patients presented with comorbidities such as hepatitis B, diabetes, tuberculosis, cardiovascular and cerebrovascular diseases, and immune system disorders. Among the cohort, 183 patients underwent induction chemotherapy followed by concurrent chemoradiotherapy, whereas 24 patients received only concurrent chemoradiotherapy. Additionally, 40 patients, constituting 10.5% of the sample, were identified as malnourished based on their BMI. The criteria for diagnosing sarcopenia were an SMI\u0026thinsp;\u0026lt;\u0026thinsp;38 cm2/m2 for females and an SMI\u0026thinsp;\u0026lt;\u0026thinsp;51 cm2/m2 for males. In the training cohort, a total of 169 patients (44.4%) were assigned to the sarcopenia group, while 212 patients (55.6%) were allocated to the non-sarcopenia group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient demographics and clinical characteristics of the training cohort and the validation cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;381)(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;164)(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e285 (74.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111 (67.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;46 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e188 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90 (54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;46 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193 (50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74 (45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165 (43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e174 (45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46 (28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168 (44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTNM clinical stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162 (42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77 (47.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e219 (57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87 (53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003esarcopenia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e212(55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100 (61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e299 (78.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e126 (76.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(kg/m2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17(10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e341(89.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147(89.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol Consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e293 (76.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125 (76.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e246 (64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107 (65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInduction Chemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e183 (88.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78 (87.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Sarcopenia and Clinical Characteristics\u003c/h2\u003e \u003cp\u003eIn the training cohort, an analysis revealed that among patients classified as malnourished with a BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m2, there were 40 cases (10.5%), with 26 (65.0%) identified as sarcopenia patients and 14 (41.9%) as non-sarcopenia patients. Conversely, there were 341 cases (89.5%) of patients with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;18.5 kg/m2, of which 143 (41.9%) were sarcopenia patients and 198 (58.1%) were non-sarcopenia patients. Additionally, within the subgroup aged\u0026thinsp;\u0026ge;\u0026thinsp;46 years, there were 119 (61.7%) sarcopenia patients and 74 (38.3%) non-sarcopenia patients. In clinical staging, there were 119 (54.3%) sarcopenia patients in stage IVa, and 100 (46.7%) non-sarcopenia patients. Among patients with complications, there were 47 (57.3%) sarcopenia patients and 35 (42.7%) non-sarcopenia patients. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSarcopenia and Clinical Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003esarcopenia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-sarcopenia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal(n%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e381 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e212 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;46 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138 (73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;46 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193 (50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119 (61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTNM clinical stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162 (42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e112 (69.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219 (57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119 (54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e299 (78.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122(40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e177 (59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47 (57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35 (42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(kg/m2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14(41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341 (89.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143(41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e198(58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Survival Analysis\u003c/h2\u003e \u003cp\u003eThe training cohort of 381 patients had a median follow-up duration of 53 months (range: 2-100 months). The observed 5-year overall survival (OS) rate was 74.3%. Survival curves indicated a significantly lower OS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) in the sarcopenia group compared to the non-sarcopenia group. Likewise, patients with older age, later T stage, later N stage, later TNM stage, and comorbidity exhibited notably lower OS rates (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Univariate and multivariate analysis of OS\u003c/h2\u003e \u003cp\u003eIn the training cohort, the univariate analysis identified several variables significantly associated with poorer OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). These included older age (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.93, 95% \u003cem\u003eCI\u003c/em\u003e: 1.29\u0026ndash;2.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), higher T stage (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.75, 95% \u003cem\u003eCI\u003c/em\u003e: 1.27\u0026ndash;2.41, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), higher N stage (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.95, 95% \u003cem\u003eCI\u003c/em\u003e: 1.51\u0026ndash;2.54, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), higher TNM clinical stage (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.91, 95% \u003cem\u003eCI\u003c/em\u003e: 1.25\u0026ndash;2.91, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), sarcopenia group (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.33, 95% \u003cem\u003eCI\u003c/em\u003e: 2.19\u0026ndash;5.05, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and a history of comorbidities (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.31, 95% \u003cem\u003eCI\u003c/em\u003e: 1.54\u0026ndash;3.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Regrettably, no significant associations were observed between OS and factors including gender, BMI, history of alcohol consumption, smoking history, and induction chemotherapy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe multivariate analysis confirmed several variables independently prognostic for OS(Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB): higher T stage (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.15, 95% \u003cem\u003eCI\u003c/em\u003e: 1.3\u0026ndash;3.57, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), higher N stage (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.15, 95% \u003cem\u003eCI\u003c/em\u003e: 1.56\u0026ndash;2.95, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), sarcopenia group (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.46, 95% \u003cem\u003eCI\u003c/em\u003e: 1.58\u0026ndash;3.83, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and a history of comorbidities (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.76, 95% \u003cem\u003eCI\u003c/em\u003e: 1.16\u0026ndash;2.67, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). Regrettably, no significant associations were observed between OS and factors including age and TNM clinical stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Development and Validation of Nomograms\u003c/h2\u003e \u003cp\u003eUtilizing these variables, we constructed Nomograms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) for predicting OS. Each variable subtype received a score based on a scoring system, and their cumulative scores formed a total score for prognostication. The Nomograms in the training cohort showed a C-index of 0.731 for OS prediction, while the validation cohort showed a C-index of 0.72 for OS prediction. In the training cohort and validation cohort, the calibration plot depicts the strong performance of the Nomogram model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). Additionally, we compared the predictive accuracy of the Nomograms against the TNM staging system for 5-year OS. DCA results indicated a higher net benefit of the Nomograms over the TNM clinical stage for predicting 5-year OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC ). Furthermore, ROC curve analysis demonstrated that the Nomograms predicting 5-year OS (AUC\u0026thinsp;=\u0026thinsp;0.718, 95% CI: 0.653\u0026ndash;0.783) showed higher sensitivity and specificity compared to the TNM clinical stage predicting 5-year OS (AUC\u0026thinsp;=\u0026thinsp;0.587, 95% CI: 0.526\u0026ndash;0.647, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMalignant tumors present a chronic wasting disease, predisposing patients to a heightened risk of malnutrition owing to abnormal tumor cell metabolism and adverse reactions from anti-tumor therapies.\u003csup\u003e24\u003c/sup\u003e The severe impact of malnutrition significantly hampers the effectiveness of anti-tumor treatments, exerting a pervasive influence throughout the disease trajectory and remaining a primary adverse factor in the clinical outcomes of cancer patients.\u003csup\u003e24\u003c/sup\u003e The location of the tumor is a primary determinant of malnutrition, with head and neck cancer being particularly common in this regard.\u003csup\u003e25\u003c/sup\u003e Therefore, timely and precise diagnosis of malnutrition holds crucial importance in enhancing nutritional status and improving prognostic outcomes.\u003c/p\u003e \u003cp\u003eThe skeletal muscle mass index (SMI) serves as a key indicator in evaluating the nutritional status of patients, with low skeletal muscle mass commonly identified as sarcopenia.\u003csup\u003e13,14\u003c/sup\u003e The present study found that patients with sarcopenia exhibited significantly lower OS rates compared to those without sarcopenia, suggesting sarcopenia as a negative prognostic factor for individuals with laNPC. The underlying reasons for this association may be multifactorial.\u003csup\u003e26,27\u003c/sup\u003e Given the crucial role muscles play in metabolic regulation and immunity, muscle atrophy increases the risk of tumor progression.\u003csup\u003e12,15\u003c/sup\u003e Skeletal muscles can function as an endocrine organ, releasing myokines, cytokines, and proteins via autocrine, paracrine, and endocrine pathways, influencing signaling between muscles and other systems.\u003csup\u003e28,29\u003c/sup\u003e Additionally, the microenvironment of skeletal muscles comprises neutrophils, monocytes, and T cells, participating in metabolic processes, wherein sarcopenia in patients might imply compromised host immune defense, leading to reduced sensitivity to immune therapy.\u003csup\u003e30\u003c/sup\u003e Furthermore, sarcopenia is primarily induced by tumor metabolism, where the additional protein consumed by tumors reduces the available protein for other tissues, leading to sarcopenia and cachexia, ultimately resulting in adverse prognoses.\u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrevious research has demonstrated consistent findings. A study conducted by Liu et al. revealed that sarcopenia serves as a distinct risk factor for OS and PFS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, individuals with sarcopenia exhibited inferior OS (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.00, \u003cem\u003e95% CI\u003c/em\u003e: 1.54\u0026thinsp;~\u0026thinsp;2.60, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PFS (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.67, \u003cem\u003e95% CI\u003c/em\u003e: 1.35\u0026thinsp;~\u0026thinsp;2.07, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) outcomes compared to their non-sarcopenic counterparts.\u003csup\u003e17\u003c/sup\u003e In the present study, subgroup analysis sarcopenia and clinical characteristics, it was observed that advanced age, TNM clinical stage, and comorbidities were correlated with a heightened prevalence of sarcopenia. This suggests that individuals with adverse prognostic factors are at an elevated risk of malnutrition. This assertion aligns with the perspectives of prior researchers, indicating that the later stages of disease progression are associated with an increased susceptibility to malnutrition due to the interplay of factors such as tumor burden, inflammatory response, decreased caloric consumption, and impaired nutrient absorption.\u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn the current study, an examination was conducted on the influence of BMI on the prognosis of NPC. No significant association was found, which is an intriguing phenomenon. BMI is widely used to assess the nutritional status of adults. However, the relationship between pretreatment BMI and prognosis in NPC patients remains unclear. This may be because BMI does not accurately reflect the composition of body constituents and their changes: it cannot distinguish between fat and muscle mass, nor can it differentiate between fluid volume accumulation in the interstitium and actual body tissue.\u003csup\u003e32\u003c/sup\u003e Pan et al. conducted a study to evaluate the nutritional status of patients diagnosed with laNPC.\u003csup\u003e33\u003c/sup\u003e The findings revealed that a minority of patients (3.8%) had a BMI below 18.5 kg/m2, while a significant proportion (46.9%) were identified as being at nutritional risk based on the NRS 2002 screening tool, and over half (53.1%) were classified as malnourished according to the PG-SGA assessment. Furthermore, a notable percentage (30.8%) of patients exhibited low muscle mass, with statistically significant variances observed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003csup\u003e33\u003c/sup\u003e Similar findings were noted in the current study, wherein the prevalence of sarcopenia in the training cohort was 44.4%, which was notably higher than the incidence rate of BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m2 (10.5%). Furthermore, among patients with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;18.5 kg/m2, 41.9% still exhibited sarcopenia. These results suggest that relying solely on BMI for evaluating malnutrition in patients may have certain constraints as a prognostic indicator.\u003c/p\u003e \u003cp\u003eThe study revealed significant associations between patient prognosis and various factors, including age, T stage, N stage, TNM clinical stage, and comorbidities such as hepatitis B, diabetes, cardiovascular diseases, tuberculosis, and immune system disorders. Notably, hepatitis B exhibited the highest incidence rate among the comorbidities studied, likely due to the high prevalence of chronic HBV infection in Southern China, where the HBsAg seropositivity rate in the general population ranges from 10\u0026ndash;12%.\u003csup\u003e34\u003c/sup\u003e Therefore, hepatitis B is also an important comorbidity in patients with nasopharyngeal carcinoma in Southern China. The poor prognosis observed in patients with hepatitis B may be attributed to immune dysfunction, including hepatitis B-related nephritis, elevated expression of the immune inhibitory protein programmed cell death protein 1 (PD-1) on total T cells, and reduced proliferative capacity of activated B cells, suggesting impaired immune function in individuals with hepatitis B.\u003csup\u003e35,36\u003c/sup\u003e Therefore, the interplay between hepatitis B and the immune response of NPC cells could have nuanced implications, potentially compromising the immune surveillance of malignant cell hosts in patients with NPC.\u003c/p\u003e \u003cp\u003eThe 8th Edition of the UICC/AJCC TNM staging system utilizes the parameters of \"Tumor\" (T), \"Node\" (N), and \"Metastasis\" (M) to delineate the anatomical scope of tumors. In particular, \"T\" characterizes the size and invasiveness of the primary tumor, \"N\" signifies the existence and extent of regional lymph node metastasis (LN(s)), and \"M\" denotes the presence or absence of distant metastasis.\u003csup\u003e37\u003c/sup\u003e In the current investigation, the T stage, N stage, and TNM clinical stage were identified as factors associated with patient prognosis in univariate analysis. However, in multivariate analysis, the TNM clinical stage did not demonstrate statistical significance. This lack of significance may be attributed to the interdependent nature of the T stage, N stage, and M stage in determining the TNM clinical stage, resulting in a combined influence that neutralizes their individual impact on prognosis in multivariate analysis. These findings indicate that the study population in the current research is reflective of the broader population of laNPC patients, thereby bolstering the validity of the analytical results.\u003c/p\u003e \u003cp\u003eThe 8th Edition of the UICC/AJCC TNM staging system is limited in its consideration of the biological heterogeneity of NPA patients, focusing primarily on anatomical structure.\u003csup\u003e11,38\u003c/sup\u003e The variability in patient outcomes within the same stage hinders the prognostic accuracy of the TNM staging system. The present study highlights the significant relationship between nutritional status and tumor prognosis. Consequently, a novel nomogram model was developed to forecast the overall survival of laNPC patients, and its predictive performance was compared to that of the TNM clinical stage. The findings suggest that the nomogram model demonstrates superior accuracy in prognostic prediction compared to the TNM clinical stage. This straightforward scoring system enables clinicians and patients to discern distinct subgroups of patients with varying survival risks, facilitating the selection of tailored treatment and care strategies. Enhanced follow-up measures can be implemented for high-risk patients.\u003c/p\u003e \u003cp\u003eOur study acknowledges several limitations. Primarily, the nomogram construction relied on data solely from a single center in a specific geographic region, potentially introducing bias. Thus, further validation using external datasets is imperative to establish its generalizability. Secondly, determining optimal cut-off values for the sarcopenia warrant extensive investigation through large-scale clinical studies to enhance predictive precision. Moreover, our study primarily focuses on the relationship between sarcopenia and prognosis at the onset of patient treatment, lacking exploration into their dynamic interaction throughout the treatment trajectory.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn the present study, sarcopenia was identified as a prognostic factor for patients with laNPC. Patients with sarcopenia demonstrated lower overall survival rates compared to non-sarcopenic patients. Furthermore, T stage, N stage, and comorbidities were also found to be prognostically correlated factors in this investigation. The integration of these prognostic factors into a constructed nomogram predictive model exhibited superior predictive capacity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by grants from the National Natural Science Foundation of China (No. 71964003, 81460460, 81760542, 82260467), The Natural Science Foundation of Guangxi Zhuang Autonomous Region (No. 2018JJA141048), The central government guide local science and technology development projects (ZY18057006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (2024-E178-01, \u0026nbsp;March 13, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u0026nbsp;\u003c/strong\u003eBecause of the retrospective design of the study, the requirement for informed consent for publication of human research participants was waived, \u0026nbsp;Approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (2024-E178-01, \u0026nbsp;March 13, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e Data for this study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u0026nbsp;\u003c/strong\u003eThe author(s) report no conflicts of interest in this work.\u0026nbsp;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTL: Conceptualization, Formal analysis, Investigation, Methodology, Writing \u0026ndash; original draft. GMW: Data curation, Formal analysis, Investigation. CMC: Investigation, Software, Writing \u0026ndash; original draft. LHH: Conceptualization, Methodology, Validation, Visualization. RSW: Investigation, Supervision, Validation, Writing \u0026ndash; review \u0026amp; editing. All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChang E T, Ye W, Zeng Y-X, and Adami H-O. The Evolving Epidemiology of Nasopharyngeal Carcinoma. 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Staging of nasopharyngeal carcinoma\u0026ndash;the past, the present and the future. Oral Oncol 50 (2014) 549\u0026ndash;554. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.oraloncology.2013.06.003\u003c/span\u003e\u003cspan address=\"10.1016/j.oraloncology.2013.06.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu K, and Wang J. Developing a nomogram model and prognostic analysis of nasopharyngeal squamous cell carcinoma patients: a population-based study. Journal of Cancer Research and Clinical Oncology 149 (2023) 12165\u0026ndash;12175. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00432-023-05120-3\u003c/span\u003e\u003cspan address=\"10.1007/s00432-023-05120-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sarcopenia1, Nasopharyngeal Carcinoma2, Chemoradiotherapy3, Nomogram4, prognosis5","lastPublishedDoi":"10.21203/rs.3.rs-4015258/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4015258/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eSarcopenia, as defined by the Global Leadership Initiative on Malnutrition (GLIM) consensus, serves as a diagnostic indicator for malnutrition and has been shown to influence cancer treatment and clinical results. However, the impact of sarcopenia on individuals diagnosed with nasopharyngeal carcinoma (NPC) remain insufficiently elucidated. The objective of this study was to investigate the prognostic significance of sarcopenia on the survival outcomes of NPC patients and to develop a nomogram.\u003c/p\u003e\u003ch2\u003ePatients and methods:\u003c/h2\u003e \u003cp\u003e545 patients with stage III-IVa NPC were included in this retrospective study and randomly divided into training and validation cohort (381 and 164 patients, respectively). Sarcopenia was defined using the skeletal muscle index (SMI) determined at the C3 level based on baseline MRI. The nomogram was developed utilizing a multivariable Cox model with baseline variables from the training cohort, and validated with the validation cohort. The nomogram's discriminative ability and accuracy were evaluated using the consistency index (C-index), receiver operating characteristic curve (ROC), and calibration plots, while the net benefit was assessed and compared with the TNM clinical stage through decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results of the multivariate analysis revealed that higher T stage (HR\u0026thinsp;=\u0026thinsp;2.15, 95% CI: 1.3\u0026ndash;3.57, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), higher N stage (HR\u0026thinsp;=\u0026thinsp;2.15, 95% CI: 1.56\u0026ndash;2.95, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), sarcopenia group (HR\u0026thinsp;=\u0026thinsp;2.46, 95% CI: 1.58\u0026ndash;3.83, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and a history of comorbidities (HR\u0026thinsp;=\u0026thinsp;1.76, 95% CI: 1.16\u0026ndash;2.67, P\u0026thinsp;=\u0026thinsp;0.01) were identified as independent risk factors that significantly impacted both overall survival (OS). The C-index (0.731 for the training cohort and 0.72 for the validation cohort indicated satisfactory discriminative ability of the nomogram. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts. Moreover, nomograms also showed higher outcomes of DCA and the area under the curve (AUC) compared to TNM clinical stage.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSarcopenia, T stage, N stage, and comorbidities were identified as independent prognostic factors for locally advanced NPC (laNPC). The integration of these factors into a nomogram predictive model demonstrated enhanced predictive accuracy.\u003c/p\u003e","manuscriptTitle":"Predicting the risk of sarcopenia in Nasopharyngeal Carcinoma patients: Development and assessment of a new predictive nomogram","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-19 20:21:30","doi":"10.21203/rs.3.rs-4015258/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fc33c6d3-b166-465e-b427-e9e5848fe356","owner":[],"postedDate":"March 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29540798,"name":"Biological sciences/Cancer/Head and neck cancer"},{"id":29540799,"name":"Health sciences/Biomarkers/Prognostic markers"}],"tags":[],"updatedAt":"2024-04-01T04:27:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-19 20:21:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4015258","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4015258","identity":"rs-4015258","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}